基于随机组合核集的分布式加权匹配
Distributed Weighted Matching via Randomized Composable Coresets
摘要：
最大权匹配问题是组合优化中最基本的问题之一，在数据采掘和生物信息学中有着广泛的应用。开发分布式加权匹配算法是具有挑战性的，因为这个问题的有效算法的顺序性。在本文中，我们开发了一个简单的分布式算法的问题，一般图的近似保证$2+\varepsilon$，（几乎）匹配的顺序贪心算法。该算法的一个关键优点是，它可以很容易地实现在现代并行计算框架，如MapReduce的只有两轮计算。我们还证明了我们的算法在实践中的效率在各种图形（一些有半万亿边）实现的目标值总是接近什么是可实现的集中设置。
英文摘要：
Maximum weight matching is one of the most fundamental combinatorial optimization problems with a wide range of applications in data mining and bioinformatics. Developing distributed weighted matching algorithms is challenging due to the sequential nature of efficient algorithms for this problem. In this paper, we develop a simple distributed algorithm for the problem on general graphs with approximation guarantee of $2+\varepsilon$ that (nearly) matches that of the sequential greedy algorithm. A key advantage of this algorithm is that it can be easily implemented in only two rounds of computation in modern parallel computation frameworks such as MapReduce. We also demonstrate the efficiency of our algorithm in practice on various graphs (some with half a trillion edges) by achieving objective values always close to what is achievable in the centralized setting.
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